Sparce Subspace Learning and Characteristic Based Split for Modelling Artificial Ground Freezing

Artificial Ground Freezing technique is often used to build underground structures, such as urban tunnels. modeling activities are necessary to optimize the design of the freezing process. The paper introduces a numerical solution of the phenomenon of water freezing for various soil types, using the finite element method combined with the model order reduction technique. In particular, the characteristic based split method is used as a base to build a model reduction scheme by means of Sparse Subspace Learning. The aim of this study is to develop a mathematical model able to carry out numerical results with negligible CPU time, by varying three parameters reproducing the characteristics of the soil, i.e. the porosity, the Darcy number and the Rayleigh number. The obtained results show how the soil parameters influence the freezing process and the dimension of ice wall.